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2020 – today
- 2024
[c18]Valentine Anantharaj, Takuya Kurihana, Sajal Dash, Gabriele Padovani, Sandro Fiore:
Exploring Vision Transformers on the Frontier Supercomputer for Remote Sensing and Geoscientific Applications. IGARSS 2024: 3085-3088- [c17]Sajal Dash, Isaac Lyngaas, Junqi Yin, Xiao Wang
, Romain Egele, J. Austin Ellis, Matthias Maiterth, Guojing Cong, Feiyi Wang, Prasanna Balaprakash:
Optimizing Distributed Training on Frontier for Large Language Models. ISC 2024: 1-11 - [i4]Wesley Brewer, Aditya Kashi, Sajal Dash, Aristeidis Tsaris, Junqi Yin, Mallikarjun Shankar, Feiyi Wang:
Scalable Artificial Intelligence for Science: Perspectives, Methods and Exemplars. CoRR abs/2406.17812 (2024) - 2023
- [j1]Junqi Yin, Sajal Dash, John Gounley, Feiyi Wang, Georgia D. Tourassi:
Evaluation of pre-training large language models on leadership-class supercomputers. J. Supercomput. 79(18): 20747-20768 (2023) - [c16]Sajal Dash, Mohammad Alaul Haque Monil, Junqi Yin, Ramu Anandakrishnan, Feiyi Wang:
Distributing Simplex-Shaped Nested for-Loops to Identify Carcinogenic Gene Combinations. IPDPS 2023: 974-984 - [c15]Adam Saunders, Sajal Dash, Aristeidis Tsaris, Hong-Jun Yoon:
A comparison of histopathology imaging comprehension algorithms based on multiple instance learning. Digital and Computational Pathology 2023 - [c14]Aristeidis Tsaris, Joshua Romero, Thorsten Kurth, Jacob D. Hinkle, Hong-Jun Yoon, Feiyi Wang, Sajal Dash, Georgia D. Tourassi:
Scaling Resolution of Gigapixel Whole Slide Images Using Spatial Decomposition on Convolutional Neural Networks. PASC 2023: 2:1-2:11 - [c13]Junqi Yin, Sajal Dash, Feiyi Wang, Mallikarjun Shankar:
FORGE: Pre-Training Open Foundation Models for Science. SC 2023: 81:1-81:13 - [i3]Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang, Conglong Li, Shiyang Chen, Chengming Zhang, Masahiro Tanaka, Xiaoxia Wu, Jeff Rasley, Ammar Ahmad Awan, Connor Holmes, Martin Cai, Adam Ghanem, Zhongzhu Zhou, Yuxiong He, Pete Luferenko, Divya Kumar, Jonathan A. Weyn, Ruixiong Zhang, Sylwester Klocek, Volodymyr Vragov, Mohammed AlQuraishi, Gustaf Ahdritz, Christina Floristean, Cristina Negri, Rao Kotamarthi, Venkatram Vishwanath, Arvind Ramanathan, Sam Foreman, Kyle Hippe, Troy Arcomano, Romit Maulik, Maxim Zvyagin, Alexander Brace, Bin Zhang, Cindy Orozco Bohorquez, Austin Clyde, Bharat Kale, Danilo Perez-Rivera, Heng Ma, Carla M. Mann, Michael W. Irvin, J. Gregory Pauloski, Logan T. Ward, Valérie Hayot-Sasson, Murali Emani, Zhen Xie, Diangen Lin, Maulik Shukla, Ian T. Foster, James J. Davis, Michael E. Papka, Thomas S. Brettin, Prasanna Balaprakash, Gina Tourassi, John Gounley, Heidi A. Hanson, Thomas E. Potok, Massimiliano Lupo Pasini, Kate Evans, Dan Lu, Dalton D. Lunga, Junqi Yin, Sajal Dash, Feiyi Wang, Mallikarjun Shankar, Isaac Lyngaas, Xiao Wang, Guojing Cong, Pei Zhang, Ming Fan, Siyan Liu, Adolfy Hoisie, Shinjae Yoo, Yihui Ren, William Tang, Kyle Felker, Alexey Svyatkovskiy, Hang Liu, Ashwin M. Aji, Angela Dalton, Michael J. Schulte, Karl Schulz, Yuntian Deng, Weili Nie, Josh Romero, Christian Dallago, Arash Vahdat, Chaowei Xiao, Thomas Gibbs, Anima Anandkumar, Rick Stevens:
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies. CoRR abs/2310.04610 (2023) - [i2]Xiao Wang, Isaac Lyngaas, Aristeidis Tsaris, Peng Chen, Sajal Dash, Mayanka Chandra Shekar, Tao Luo, Hong-Jun Yoon, Mohamed Wahib, John Gounley:
Ultra-Long Sequence Distributed Transformer. CoRR abs/2311.02382 (2023) - [i1]Sajal Dash, Isaac Lyngaas, Junqi Yin, Xiao Wang, Romain Egele, Guojing Cong, Feiyi Wang, Prasanna Balaprakash:
Optimizing Distributed Training on Frontier for Large Language Models. CoRR abs/2312.12705 (2023) - 2022
- [c12]Folami Alamudun, Jacob D. Hinkle, Sajal Dash, Benjamín Hernández, Aristeidis Tsaris, Hong-Jun Yoon:
Distilling Knowledge from Ensembles of Cluster-Constrained-Attention Multiple-Instance Learners for Whole Slide Image Classification. IEEE Big Data 2022: 3393-3397 - [c11]Awais Khan, Arnab K. Paul, Christopher Zimmer, Sarp Oral, Sajal Dash, Scott Atchley, Feiyi Wang:
Hvac: Removing I/O Bottleneck for Large-Scale Deep Learning Applications. CLUSTER 2022: 324-335 - [c10]Sajal Dash, Benjamín Hernández, Aristeidis Tsaris, Folami T. Alamudun, Hong-Jun Yoon, Feiyi Wang:
A Scalable Pipeline for Gigapixel Whole Slide Imaging Analysis on Leadership Class HPC Systems. IPDPS Workshops 2022: 1266-1274 - [c9]Priscilla Cho, Sajal Dash, Aristeidis Tsaris, Hong-Jun Yoon:
Image transformers for classifying acute lymphoblastic leukemia. Computer-Aided Diagnosis 2022 - [c8]Junqi Yin, Guannan Zhang, Huibo Cao, Sajal Dash, Bryan C. Chakoumakos, Feiyi Wang:
Toward an Autonomous Workflow for Single Crystal Neutron Diffraction. SMC 2022: 244-256 - 2021
- [c7]Sajal Dash, Qais Al-Hajri, Wu-chun Feng, Harold R. Garner, Ramu Anandakrishnan:
Scaling Out a Combinatorial Algorithm for Discovering Carcinogenic Gene Combinations to Thousands of GPUs. IPDPS 2021: 837-846 - [c6]Sajal Dash, Junqi Yin, Mallikarjun Shankar, Feiyi Wang, Wu-chun Feng:
Mitigating Catastrophic Forgetting in Deep Learning in a Streaming Setting Using Historical Summary. DRBSD@SC 2021: 11-18 - [c5]Pravallika Devineni, Panchapakesan Ganesh, Nikhil Sivadas, Abhijeet Dhakane, Ketan Maheshwari, Drahomira Herrmannova, Ramakrishnan Kannan, Seung-Hwan Lim, Thomas E. Potok, Jordan B. Chipka, Priyantha Mudalige, Mark Coletti, Sajal Dash, Arnab Kumar Paul, Sarp Oral, Feiyi Wang, Bill Kay, Melissa R. Allen-Dumas, Christa Brelsford, Joshua R. New, Andy Berres, Kuldeep R. Kurte, Jibonananda Sanyal, Levi Sweet, Chathika Gunaratne, Maxim A. Ziatdinov, Rama K. Vasudevan, Sergei V. Kalinin, Olivera Kotevska, Jean C. Bilheux, Hassina Z. Bilheux, Garrett E. Granroth, Thomas Proffen, Rick Riedel, Peter F. Peterson, Shruti R. Kulkarni, Kyle P. Kelley, Stephen Jesse, Maryam Parsa:
Smoky Mountain Data Challenge 2021: An Open Call to Solve Scientific Data Challenges Using Advanced Data Analytics and Edge Computing. SMC 2021: 361-382 - 2020
- [b1]Sajal Dash:
Exploring the Landscape of Big Data Analytics Through Domain-Aware Algorithm Design. Virginia Tech, Blacksburg, VA, USA, 2020 - [c4]Sajal Dash, Archi Dasgupta:
Towards a Universal Classifier for Crystallographic Space Groups: A Trickle-Down Approach to Handle Data Imbalance. SMC 2020: 465-478
2010 – 2019
- 2019
- [c3]Junqi Yin, Shubhankar Gahlot, Nouamane Laanait, Ketan Maheshwari, Jack Morrison, Sajal Dash, Mallikarjun Shankar:
Strategies to Deploy and Scale Deep Learning on the Summit Supercomputer. DLS@SC 2019: 84-94 - 2017
- [c2]Sajal Dash, Anshuman Verma, Chris North, Wu-chun Feng:
Portable Parallel Design of Weighted Multi-Dimensional Scaling for Real-Time Data Analysis. HPCC/SmartCity/DSS 2017: 10-17 - 2011
- [c1]Sajal Dash, Jack Snoeyink:
On the energy of bifurcated hydrogen bonds for protein structure prediction. BIBM Workshops 2011: 334-337
Coauthor Index
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